Status Quo Bias Research Paper

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Status Quo Bias Research Paper

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Episode 11: Status Quo Bias

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The third alternative explanation was that people have habitual bargaining behaviors, such as overstating their minimum selling price or understating their maximum bargaining price, that may spill over from strategic interactions where these behaviors are useful to the laboratory setting where they are sub-optimal. An experiment was conducted to address this by having the clearing prices selected at random. Buyers who indicated a willingness-to-pay higher than the randomly drawn price got the good, and vice versa for those who indicated a lower WTP.

Likewise, sellers who indicated a lower willingness-to-accept than the randomly drawn price sold the good and vice versa. This incentive compatible value elicitation method did not eliminate the endowment effect but did rule out habitual bargaining behavior as an alternative explanation. Income effects were ruled out by giving one third of the participants mugs, one third chocolates, and one third neither mug nor chocolate. They were then given the option of trading the mug for the chocolate or vice versa and those with neither were asked to merely choose between mug and chocolate.

Thus, wealth effects were controlled for those groups who received mugs and chocolate. This ruled out income effects as an explanation for the endowment effect. Also, since all participants in the group had the same good, it could not be considered a "trophy", eliminating the final alternative explanation. Multiple studies have questioned the existence of loss aversion.

In several studies examining the effect of losses in decision-making, no loss aversion was found under risk and uncertainty. Gal and Rucker made similar arguments. A paper by John Staddon, [18] citing Claude Bernard , pointed out that effects like loss aversion represent the average behavior of groups. There are many individual exceptions. To use these effects as something more than the results of an opinion poll means identifying the sources of variation, so that they can be demonstrated reliably in individual subjects.

Loss aversion may be more salient when people compete. Gill and Prowse provide experimental evidence that people are loss averse around reference points given by their expectations in a competitive environment with real effort. Loss attention refers to the tendency of individuals to allocate more attention to a task or situation when it involve losses than when it does not involve losses. What distinguishes loss attention from loss aversion is that it does not imply that losses are given more subjective weight or utility than gains. Moreover, under loss aversion losses have a biasing effect whereas under loss attention they can have a debiasing effect. Loss attention was proposed as a distinct regularity from loss aversion by Eldad Yechiam and Guy Hochman. Specifically, the effect of losses is assumed to be on general attention rather than plain visual or auditory attention.

The loss attention account assumes that losses in a given task mainly increase the general attentional resource pool available for that task. The increase in attention is assumed to have an inverse-U shape effect on performance following the so called Yerkes-Dodson law. Indeed, it was found that the positive effect of losses on performance in a given task was more pronounced in a task performed concurrently with another task which was primary in its importance. Some of these effects have been previously attributed to loss aversion, but can be explained by a mere attention asymmetry between gains and losses. An example is the performance advantage attributed to golf rounds where a player is under par or in a disadvantage compared to other rounds where a player is at an advantage.

Recently, studies have suggested that loss aversion mostly occur for very large losses [22] though the exact boundaries of the effect are unclear. Still, one might argue that loss aversion is more parsimonious than loss attention. Increased expected value maximization with losses — It was found that individuals are more likely to select choice options with higher expected value namely, mean outcome in tasks where outcomes are framed as losses than when they are framed as gains. Yechiam and Hochman [23] found that this effect occurred even when the alternative producing higher expected value was the one that included minor losses. Namely, a highly advantageous alternative producing minor losses was more attractive compared when it did not produce losses.

Therefore, paradoxically, in their study minor losses led to more selection from the alternative generating them refuting an explanation of this phenomenon based on loss aversion. Loss arousal — Individuals were found to display greater Autonomic Nervous System activation following losses than following equivalent gains. Importantly, this was found even for small losses and gains where individuals do not show loss aversion. Similarly, a positive effect of losses compared to equivalent gains was found on activation of midfrontal cortical networks to milliseconds after observing the outcome. Increased hot stove effect for losses — The hot stove effect is the finding that individuals avoid a risky alternative when the available information is limited to the obtained payoffs.

A relevant example proposed by Mark Twain is of a cat which jumped of a hot stove and will never do it again, even when the stove is cold and potentially contains food. Apparently, when a given option produces losses this increases the hot stove effect, [28] a finding which is consistent with the notion that losses increase attention. The out of pocket phenomenon — In financial decision making, it has been shown that people are more motivated when their incentives are to avoid losing personal resources, as opposed to gaining equivalent resources.

Traditionally, this strong behavioral tendency was explained by loss aversion. However, it could also be explained simply as increased attention. The allure of minor disadvantages — In marketing studies it has been demonstrated that products whose minor negative features are highlighted in addition to positive features are perceived as more attractive. In , experiments were conducted on the ability of capuchin monkeys to use money. After several months of training, the monkeys began showing behavior considered to reflect understanding of the concept of a medium of exchange.

They exhibited the same propensity to avoid perceived losses demonstrated by human subjects and investors. Chen, Lakshminarayanan and Santos also conducted experiments on capuchin monkeys to determine whether behavioral biases extend across species. In one of their experiments, subjects were presented with two choices that both delivered an identical payoff of one apple piece in exchange of their coins. Experimenter 1 displayed one apple piece and gave that exact amount. Experimenter 2 displayed two applies pieces initially but always removed one piece before delivering the remaining apple piece to the subject. Therefore, identical payoffs are yielded regardless of which experimenter the subject traded with.

It was found a that subjects strongly preferred the experimenter who initially displayed only one apple piece, even though both experimenters yielded the same out come of one apple piece. This study suggests that capuchins weighted losses more heavily than equivalent gains. Expectation-based loss aversion is a phenomenon in behavioral economics. When the expectations of an individual fail to match reality, they lose an amount of utility from the lack of experiencing fulfillment of these expectations. Participants were asked to participate in an iterative money-making task given the possibilities that they would receive either an accumulated sum for each round of "work", or a predetermined amount of money.

They chose to stop when the values were equal as no matter which random result they received, their expectations would be matched. Participants were reluctant to work for more than the fixed payment as there was an equal chance their expected compensation would not be met. Loss aversion experimentation has most recently been applied within an educational setting in an effort to improve achievement within the U. In this latest experiment, Fryer et al.

This study was performed in the city of Chicago Heights within nine K-8 urban schools, which included 3, students. The control group followed the traditional merit pay process of receiving "bonus pay" at the end of the year based on student performance on standardized exams. However, the experimental groups received a lump sum given at beginning of the year, that would have to be paid back. According to the authors, 'this suggests that there may be significant potential for exploiting loss aversion in the pursuit of both optimal public policy and the pursuit of profits'.

Thomas Amadio, superintendent of Chicago Heights Elementary School District , where the experiment was conducted, stated that "the study shows the value of merit pay as an encouragement for better teacher performance". In earlier studies, both bidirectional mesolimbic responses of activation for gains and deactivation for losses or vica versa and gain or loss-specific responses have been seen. While reward anticipation is associated with ventral striatum activation, [40] [41] negative outcome anticipation engages the amygdala. To start, it is telling how little data is made publicly available on how these scores vary by race. However, federal law requires lenders to collect data on race for home mortgage applications, so we do have access to some data.

As shown in the figure below, the differences are stark. Among people trying to buy a home, generally a wealthier and older subset of Americans, white homebuyers have an average credit score 57 points higher than Black homebuyers and 33 points higher than Hispanic homebuyers. The distribution of credit scores is also sharply unequal: More than 1 in 5 Black individuals have FICOs below , as do 1 in 9 among the Hispanic community, while the same is true for only 1 out of every 19 white people. Higher credit scores allow borrowers to access different types of loans and at lower interest rates. One suspects the gaps are even broader beyond those trying to buy a home. If FICO were invented today, would it satisfy a disparate impact test? I have described FICO as the out of tune oboe to which the rest of the financial orchestra tunes.

New data and algorithms are not grandfathered and are subject to the disparate impact test. The result is a double standard whereby new technology is often held to a higher standard to prevent bias than existing methods. This has the effect of tilting the field against new data and methodologies, reinforcing the existing system. Lenders are required to tell consumers why they were denied. Explaining the rationale provides a paper trail to hold lenders accountable should they be engaging in discrimination.

It also provides the consumer with information to allow them to correct their behavior and improve their chances for credit. To start, imagine a trade-off between accuracy represented on the y-axis and bias represented on the x-axis. Any potential change needs to be considered against the status-quo—not an ideal world of no bias nor complete accuracy. This forces policymakers to consider whether the adoption of a new system that contains bias, but less than that in the current system, is an advance. It may be difficult to embrace an inherently biased framework, but it is important to acknowledge that the status quo is already highly biased.

Thus, rejecting new technology because it contains some level of bias does not mean we are protecting the system against bias. To the contrary, it may mean that we are allowing a more biased system to perpetuate. As shown in the figure above, the bottom left corner quadrant III is one where AI results in a system that is more discriminatory and less predictive. Regulation and commercial incentives should work together against this outcome. It may be difficult to imagine incorporating new technology that reduces accuracy, but it is not inconceivable, particularly given the incentives in industry to prioritize decision-making and loan generation speed over actual loan performance as in the subprime mortgage crisis. Another potential occurrence of policy moving in this direction is the introduction of inaccurate data that may confuse an AI into thinking it has increased accuracy when it has not.

The existing credit reporting system is rife with errors : 1 out of every 5 people may have material error on their credit report. New errors occur frequently—consider the recent mistake by one student loan servicer that incorrectly reported 4. The data used in the real world are not as pure as those model testing. Market incentives alone are not enough to produce perfect accuracy; they can even promote inaccuracy given the cost of correcting data and demand for speed and quantity. As one study from the Federal Reserve Bank of St. The top right quadrant I represents incorporation of AI that increases accuracy and reduces bias. At first glance, this should be a win-win. Industry allocates credit in a more accurate manner, increasing efficiency.

Consumers enjoy increased credit availability on more accurate terms and with less bias than the existing status quo. This optimistic scenario is quite possible given that a significant source of existing bias in lending stems from the information used. One prominent example of a win-win system is the use of cash-flow underwriting. Cash-flow analysis does have some level of bias as income and wealth are correlated with race, gender, and other protected classes. However, because income and wealth are acceptable existing factors, the current fair-lending system should have little problem allowing a smarter use of that information.

Ironically, this new technology meets the test because it uses data that is already grandfathered. That is not the case for other AI advancements. Retrieved 8 February The Optimal Environment: Part Four. Retrieved ISBN Boundless, 27 Jun. Retrieved 08 Feb. Categories : Latin words and phrases Change Cognitive inertia Assumption reasoning. Hidden categories: Articles with short description Short description matches Wikidata Articles containing Latin-language text. Namespaces Article Talk.

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